At a Glance
- Tasks: Own the full research pipeline in a dynamic trading environment, tackling complex market problems.
- Company: Join a leading firm with unmatched data and compute infrastructure for innovative research.
- Benefits: Competitive salary, collaborative culture, and access to cutting-edge technology.
- Other info: Collaborative team environment with opportunities for growth across major financial hubs.
- Why this job: Make a real impact by developing predictive models and working with live capital.
- Qualifications: 3-6 years in systematic trading, strong statistical skills, and Python proficiency.
The predicted salary is between 60000 - 80000 £ per year.
This is a systematic quant researcher role within a cross-asset trading environment spanning holding periods from hours to weeks. You will own the full research pipeline: signal generation, testing, portfolio-level analysis, and work with live capital. The asset universe is broad: equities, futures, FX, credit, commodities, and ETF structures all feature. The problems are genuinely hard: signal decay, regime sensitivity, execution friction, and cross-asset correlation structure all matter here. You will be expected to form views, test them rigorously, and defend them.
You will have access to data and compute infrastructure at a scale very few firms can match. Research custom trading models to compete with the scale of frontier LLMs, consuming trillions of tokens of market data. Experimentation here is not constrained by tooling, it is constrained by the quality of your ideas.
Research here is a shared endeavour, not a collection of siloed books. Every researcher has full visibility into every active strategy's code. There are no black boxes, no protected territories. The expectation is that collective understanding produces better research than individual ownership. Strategies are sized for their contribution to the portfolio as a whole, not as standalone entities. That means your work is evaluated at the system level, which rewards researchers who think carefully about covariance, capacity, and cross-strategy interaction, not just isolated backtest Sharpe.
You have 3-6 years of experience in a systematic trading environment, a hedge fund, prop trading firm, or closely related research role. You have built and shipped predictive models against real market data, not just in simulation.
- Core requirements:
- Strong statistical foundations: time-series analysis, factor modelling, signal research
- Python proficiency; C++ experience strongly preferred (you will be interacting with C++ day to day)
- Experience across more than one asset class, or a clear track record in one with genuine appetite to work cross-asset
- Ability to take a research idea from hypothesis to backtested strategy to production-ready code
- Comfort operating in an environment where your work is visible and subject to peer scrutiny
- The right mindset: You are intellectually honest about what your models do and don’t explain. You are more interested in understanding market structure than in protecting alpha. You find the idea of a shared codebase appealing rather than threatening.
London is a focus area, but realistically anywhere across the major financial hubs (NYC, Singapore, Hong Kong, Chicago).
Quantitative Researcher employer: Augmentti
As a Quantitative Researcher at our firm, you will thrive in a collaborative and innovative environment that values shared knowledge and rigorous testing of ideas. With access to unparalleled data and compute infrastructure, you will have the opportunity to develop cutting-edge trading models across a diverse asset universe, all while being part of a culture that prioritises collective success over individual silos. Our London location, along with other major financial hubs, offers a dynamic backdrop for your professional growth, ensuring that your contributions are recognised and rewarded within a forward-thinking team.
StudySmarter Expert Advice🤫
We think this is how you could land Quantitative Researcher
✨Tip Number 1
Network like a pro! Reach out to folks in the industry, attend meetups, and connect with other quantitative researchers. You never know who might have the inside scoop on job openings or can refer you directly.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your predictive models and research projects. This is your chance to demonstrate your expertise in Python and C++, and how you've tackled real market data challenges.
✨Tip Number 3
Prepare for interviews by diving deep into the latest trends in systematic trading. Brush up on your statistical foundations and be ready to discuss your thought process behind your models. We want to see how you think!
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets seen. Plus, we love seeing candidates who are proactive about their job search.
We think you need these skills to ace Quantitative Researcher
Some tips for your application 🫡
Show Your Research Skills:Make sure to highlight your experience in systematic trading and research. We want to see how you've tackled complex problems like signal decay and execution friction, so share specific examples of your work with predictive models and real market data.
Be Clear About Your Technical Skills:We’re looking for strong Python skills and ideally some C++ experience. Don’t just list these skills; show us how you’ve used them in your previous roles. A solid understanding of statistical foundations is key, so make that clear in your application.
Emphasise Collaboration:Since our research is a shared endeavour, it’s important to convey your ability to work in a team. Talk about how you’ve collaborated with others on strategies and how you value collective understanding over individual ownership.
Apply Through Our Website:We encourage you to apply directly through our website. It’s the best way for us to receive your application and ensures you’re considered for the role. Plus, it shows you’re keen to be part of our community!
How to prepare for a job interview at Augmentti
✨Know Your Models Inside Out
Make sure you can explain your predictive models in detail. Be ready to discuss how you've built and tested them against real market data, not just simulations. This shows that you understand the practical implications of your work.
✨Embrace Collaboration
Since the role emphasises shared research, be prepared to talk about how you’ve worked in a team environment. Highlight experiences where collective understanding led to better outcomes, and express your enthusiasm for a transparent codebase.
✨Demonstrate Statistical Savvy
Brush up on your statistical foundations, especially time-series analysis and factor modelling. Be ready to discuss specific techniques you've used and how they apply to the challenges mentioned in the job description, like signal decay and execution friction.
✨Show Your Cross-Asset Knowledge
If you have experience across multiple asset classes, make it a focal point in your interview. If not, demonstrate your genuine interest and readiness to dive into different areas, showing that you're adaptable and eager to learn.
